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Data Mining Software for Android

1.1.7 for Android

Darren Yates

La descripción de DataLearner

DataLearner is an easy-to-use tool for data mining and knowledge discovery from your own compatible ARFF and CSV-formatted training datasets (* see below). It’s fully self-contained, requires no external storage or network connectivity – it builds models directly on your phone or tablet. This is not a training course or book - it is a genuine machine-learning-based data mining app.

>> ARFF and CSV support <<
Training datasets must conform to either the Weka ARFF format or CSV (comma-separated variable). CSV files must have the following features:
* must include a header row
* class attribute is initially set as last column

>> Force class attribute to nominal <<
Most of DataLearner's algorithms expect nominal/categorical class attributes and using a numeric class attribute will cause most algorithms to fail. The new 'force class attribute to nominal' overcomes this, however, nominal class attributes with too many distinct values may use up too much RAM.

*** NEWS! DataLearner research has been selected for presentation at ADMA 2019 (15th International Conference on Advanced Data Mining and Applications) and will be published in 'Lecture Notes in Artificial Intelligence' (Springer) ***

DataLearner features classification, association and clustering algorithms from the open-source Weka (Waikato Environment for Knowledge Analysis) package, plus new algorithms developed by the Data Science Research Unit (DSRU) at Charles Sturt University. Combined, the app provides 42 machine-learning/data-mining algorithms, including RandomForest, C4.5 (J48) and NaiveBayes.

DataLearner collects no information – it requires access to your device storage simply to load your datasets and build your machine-learning models.

DataLearner is being used as a teaching tool in the ITC573 Data and Knowledge Engineering subject for the Master of Information Technology post-graduate degree at Charles Sturt University.

Get the resources:
GPL3-licensed source code on Github:
Quick video on YouTube:
Research paper on arXiv:
AusDM 2018 conference paper that initiated DataLearner:

Researchers, if you use this app in research applications, please cite the research papers above. Thanks.

Machine-learning algorithms include:
• Bayes – BayesNet, NaiveBayes
• Functions – Logistic, SimpleLogistic, MultiLayerPerceptron (Neural Network)
• Lazy – IBk (K Nearest Neighbours), KStar
• Meta – AdaBoostM1, Bagging, LogitBoost, MultiBoostAB, Random Committee, RandomSubSpace, RotationForest
• Rules – Conjunctive Rule, Decision Table, DTNB, JRip, OneR, PART, Ridor, ZeroR
• Trees – ADTree, BFTree, DecisionStump, ForestPA, J48 (C4.5), LADTree, Random Forest, RandomTree, REPTree, SimpleCART, SPAARC, SysFor.
• Clusterers – DBSCAN, Expectation Maximisation (EM), Farthest-First, FilteredClusterer, SimpleKMeans
• Associations – Apriori, FilteredAssociator, FPGrowth

>> Where to find dataset files? <<
DataLearner comes with a built-in demo dataset called 'rain.csv', but you'll also find plenty of datasets at the OpenML website - including the popular 'ecoli' set ( Download the ARFF versions to your phone and load them into DataLearner to build models from. Watch our new video tutorial -

This software is supplied AS-IS - while it has been tested, no warranty is implied or given.

DataLearner 1.1.7 Actualizar

* Enabled View Details/Confusion Matrix button after no-CV model build only.
* Enabled all trees in Random Forest to appear in Confusion Matrix/Model output.
* Added copy-paste to clipboard of Confusion Matrix/Model output.
* updated error message to suggest using 'Force class attribute to nominal' button on Load screen.
*fixed introduced bug preventing some statistics from appearing with numeric-class datasets.

DataLearner Tags

Additional Information

Categoría: Gratis Productividad APP

Fecha De Publicación:

Uploaded by: ابومحمد الرفيع

Última Versión: 1.1.7Solicitar DataLearner Actualización

Available on: Conseguir DataLearner desde Google Play

Requisitos: Android 4.4+

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